A Predictive Symptoms-based System using Support Vector Machines to enhanced Classification Accuracy of Malaria and Typhoid Coinfection

Abstract

High costs of medical equipment and insufficient number of medical specialists have immensely contributed to the increment of death rate especially in rural areas of most developing countries. According to Roll Back Malaria there are 300 million acute cases of malaria per year worldwide, causing more than one million deaths. About 90% of these deaths happen in Africa, majorly in young children. Besides malaria when tested; a large number is coinfected with typhoid. Most often, symptoms of malaria and typhoid fevers do have common characteristics and clinicians do have difficulties in distinguishing them. For instance in Nigeria the existing diagnostic systems for malaria and typhoid in rural settlements are inefficient thereby making the result to be inaccurate and resulting to treatment of wrong ailments. Therefore in this paper, a predictive symptoms-based system for malaria and typhoid coinfection using Support Vector Machines (SVMs) is proposed for an improved classification results and the system is implemented using Microsoft Visual Basic 2013. Relatively high performance accuracy was achieved when tested on a reserved data set collected from a hospital. Hence the system will be of a great significant use in terms of affordable and quality health care services especially in rural settlement as an alternative and a reliable diagnostic system for the ailments.

Ensemble Feed-Forward Neural Network and Support Vector Machine for Prediction of Multiclass Malaria Infection

“…The limitation of the study was that several performance metrics were needed to be employed to ascertain the correctness of the system. Moreover, the effects of global thresholding and climatic conditions were not considered (Aminu et al, 2016).…”